Bibliographic record
Abstract
In order to solve the problem of inaccurate pose estimation by the classical direct linear transformation(DLT) and efficient perspective-n-point(EPNP) methods, an improved method for pose estimation is proposed. The computation process of the DLT method is optimized for easy solving. The nonlinear optimization is introduced to improve its accuracy. A proper cost function is proposed based on the Levenberg-Marquardt(LM) algorithm for solving Jocabian matrix easily. The Li group and Li algebra are introduced for representing the tiny transformation of the pose matrix, which simplifies the solution of Jocabian matrix and iterative process of optimization. The experimental results show that the proposed method is much more accurate than the DLT and the EPNP methods, and is more accurate than the DLT + numerical value-based LM algorithm. The total mean image re-projection error is 0.269 0 pixel. The time consuming experiments indicate that the proposed method needs less time compared with the DLT + numeriacl value-based LM algorithm. Its total average time is 67.48 ms per frame. These prove that the proposed method has comprehensively good performance in precision and time cost, which has good practical value.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".